





'''simple docstring'''






import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
					import faiss
@require_faiss
class 		lowerCamelCase	(   _A							):



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= tempfile.mkdtemp()
			lowerCAmelCase  :     int       						= 8

			# DPR tok
			lowerCAmelCase  :     Optional[int]       						= [
			    "[UNK]",
			    "[CLS]",
			    "[SEP]",
			    "[PAD]",
			    "[MASK]",
			    "want",
			    "##want",
			    "##ed",
			    "wa",
			    "un",
			    "runn",
			    "##ing",
			    ",",
			    "low",
			    "lowest",
			]
			lowerCAmelCase  :     Union[str, Any]       						= os.path.join(self.tmpdirname		,   "dpr_tokenizer"						)
			os.makedirs(a_		,   exist_ok=a_						)
			lowerCAmelCase  :     Optional[Any]       						= os.path.join(a_		,   DPR_VOCAB_FILES_NAMES["vocab_file"]						)
			with open(self.vocab_file		,   "w"		,   encoding="utf-8"						) as vocab_writer:
					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)

			# BART tok
			lowerCAmelCase  :     Dict       						= [
			    "l",
			    "o",
			    "w",
			    "e",
			    "r",
			    "s",
			    "t",
			    "i",
			    "d",
			    "n",
			    "\u0120",
			    "\u0120l",
			    "\u0120n",
			    "\u0120lo",
			    "\u0120low",
			    "er",
			    "\u0120lowest",
			    "\u0120newer",
			    "\u0120wider",
			    "<unk>",
			]
			lowerCAmelCase  :     Any       						= dict(zip(a_		,   range(len(a_						)						)						)						)
			lowerCAmelCase  :     Tuple       						= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
			lowerCAmelCase  :     Any       						= {"unk_token": "<unk>"}

			lowerCAmelCase  :     Union[str, Any]       						= os.path.join(self.tmpdirname		,   "bart_tokenizer"						)
			os.makedirs(a_		,   exist_ok=a_						)
			lowerCAmelCase  :     List[str]       						= os.path.join(a_		,   BART_VOCAB_FILES_NAMES["vocab_file"]						)
			lowerCAmelCase  :     int       						= os.path.join(a_		,   BART_VOCAB_FILES_NAMES["merges_file"]						)
			with open(self.vocab_file		,   "w"		,   encoding="utf-8"						) as fp:
					fp.write(json.dumps(a_						) + "\n"						)
			with open(self.merges_file		,   "w"		,   encoding="utf-8"						) as fp:
					fp.write("\n".join(a_						)						)



	def 	_lowerCamelCase     ( self						):
			return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,   "dpr_tokenizer"						)						)



	def 	_lowerCamelCase     ( self						):
			return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,   "dpr_tokenizer"						)						)



	def 	_lowerCamelCase     ( self						):
			return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,   "bart_tokenizer"						)						)



	def 	_lowerCamelCase     ( self						):
			shutil.rmtree(self.tmpdirname						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= Dataset.from_dict(
			    {
			        "id": ["0", "1"],
			        "text": ["foo", "bar"],
			        "title": ["Foo", "Bar"],
			        "embeddings": [np.ones(self.retrieval_vector_size						), 2 * np.ones(self.retrieval_vector_size						)],
			    }						)
			dataset.add_faiss_index("embeddings"		,   string_factory="Flat"		,   metric_type=faiss.METRIC_INNER_PRODUCT						)
			return dataset



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= self.get_dummy_dataset()
			lowerCAmelCase  :     Union[str, Any]       						= RagConfig(
			    retrieval_vector_size=self.retrieval_vector_size		,   question_encoder=DPRConfig().to_dict()		,   generator=BartConfig().to_dict()		,   )
			with patch("transformers.models.rag.retrieval_rag.load_dataset"						) as mock_load_dataset:
					lowerCAmelCase  :     List[str]       						= dataset
					lowerCAmelCase  :     Tuple       						= RagRetriever(
					    a_		,   question_encoder_tokenizer=self.get_dpr_tokenizer()		,   generator_tokenizer=self.get_bart_tokenizer()		,   )
			return retriever



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     List[str]       						= self.get_dummy_dataset()
			lowerCAmelCase  :     str       						= RagConfig(
			    retrieval_vector_size=self.retrieval_vector_size		,   question_encoder=DPRConfig().to_dict()		,   generator=BartConfig().to_dict()		,   index_name="custom"		,   )
			if from_disk:
					lowerCAmelCase  :     List[Any]       						= os.path.join(self.tmpdirname		,   "dataset"						)
					lowerCAmelCase  :     List[Any]       						= os.path.join(self.tmpdirname		,   "index.faiss"						)
					dataset.get_index("embeddings"						).save(os.path.join(self.tmpdirname		,   "index.faiss"						)						)
					dataset.drop_index("embeddings"						)
					dataset.save_to_disk(os.path.join(self.tmpdirname		,   "dataset"						)						)
					del dataset
					lowerCAmelCase  :     int       						= RagRetriever(
					    a_		,   question_encoder_tokenizer=self.get_dpr_tokenizer()		,   generator_tokenizer=self.get_bart_tokenizer()		,   )
			else:
					lowerCAmelCase  :     Dict       						= RagRetriever(
					    a_		,   question_encoder_tokenizer=self.get_dpr_tokenizer()		,   generator_tokenizer=self.get_bart_tokenizer()		,   index=CustomHFIndex(config.retrieval_vector_size		,   a_						)		,   )
			return retriever



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= Dataset.from_dict(
			    {
			        "id": ["0", "1"],
			        "text": ["foo", "bar"],
			        "title": ["Foo", "Bar"],
			        "embeddings": [np.ones(self.retrieval_vector_size + 1						), 2 * np.ones(self.retrieval_vector_size + 1						)],
			    }						)
			dataset.add_faiss_index("embeddings"		,   string_factory="Flat"		,   metric_type=faiss.METRIC_INNER_PRODUCT						)

			lowerCAmelCase  :     List[str]       						= os.path.join(self.tmpdirname		,   "hf_bert_base.hnswSQ8_correct_phi_128.c_index"						)
			dataset.save_faiss_index("embeddings"		,   index_file_name + ".index.dpr"						)
			pickle.dump(dataset["id"]		,   open(index_file_name + ".index_meta.dpr"		,   "wb"						)						)

			lowerCAmelCase  :     int       						= os.path.join(self.tmpdirname		,   "psgs_w100.tsv.pkl"						)
			lowerCAmelCase  :     List[Any]       						= {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
			pickle.dump(a_		,   open(a_		,   "wb"						)						)

			lowerCAmelCase  :     List[Any]       						= RagConfig(
			    retrieval_vector_size=self.retrieval_vector_size		,   question_encoder=DPRConfig().to_dict()		,   generator=BartConfig().to_dict()		,   index_name="legacy"		,   index_path=self.tmpdirname		,   )
			lowerCAmelCase  :     Any       						= RagRetriever(
			    a_		,   question_encoder_tokenizer=self.get_dpr_tokenizer()		,   generator_tokenizer=self.get_bart_tokenizer()						)
			return retriever



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= 1
			lowerCAmelCase  :     Tuple       						= self.get_dummy_canonical_hf_index_retriever()
			lowerCAmelCase  :     Optional[int]       						= np.array(
			    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= retriever.retrieve(a_		,   n_docs=a_						)
			self.assertEqual(retrieved_doc_embeds.shape		,   (2, n_docs, self.retrieval_vector_size)						)
			self.assertEqual(len(a_						)		,   2						)
			self.assertEqual(sorted(doc_dicts[0]						)		,   ["embeddings", "id", "text", "title"]						)
			self.assertEqual(len(doc_dicts[0]["id"]						)		,   a_						)
			self.assertEqual(doc_dicts[0]["id"][0]		,   "1"						)  # max inner product is reached with second doc
			self.assertEqual(doc_dicts[1]["id"][0]		,   "0"						)  # max inner product is reached with first doc
			self.assertListEqual(doc_ids.tolist()		,   [[1], [0]]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= self.get_dummy_canonical_hf_index_retriever()
			with tempfile.TemporaryDirectory() as tmp_dirname:
					with patch("transformers.models.rag.retrieval_rag.load_dataset"						) as mock_load_dataset:
							lowerCAmelCase  :     Optional[int]       						= self.get_dummy_dataset()
							retriever.save_pretrained(a_						)
							lowerCAmelCase  :     str       						= RagRetriever.from_pretrained(a_						)
							self.assertIsInstance(a_		,   a_						)
							lowerCAmelCase  :     Optional[int]       						= np.array(
							    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
							lowerCAmelCase  :     List[Any]       						= retriever.retrieve(a_		,   n_docs=1						)
							self.assertTrue(out is not None						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= 1
			lowerCAmelCase  :     List[str]       						= self.get_dummy_custom_hf_index_retriever(from_disk=a_						)
			lowerCAmelCase  :     int       						= np.array(
			    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= retriever.retrieve(a_		,   n_docs=a_						)
			self.assertEqual(retrieved_doc_embeds.shape		,   (2, n_docs, self.retrieval_vector_size)						)
			self.assertEqual(len(a_						)		,   2						)
			self.assertEqual(sorted(doc_dicts[0]						)		,   ["embeddings", "id", "text", "title"]						)
			self.assertEqual(len(doc_dicts[0]["id"]						)		,   a_						)
			self.assertEqual(doc_dicts[0]["id"][0]		,   "1"						)  # max inner product is reached with second doc
			self.assertEqual(doc_dicts[1]["id"][0]		,   "0"						)  # max inner product is reached with first doc
			self.assertListEqual(doc_ids.tolist()		,   [[1], [0]]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= self.get_dummy_custom_hf_index_retriever(from_disk=a_						)
			with tempfile.TemporaryDirectory() as tmp_dirname:
					retriever.save_pretrained(a_						)
					lowerCAmelCase  :     Any       						= RagRetriever.from_pretrained(a_						)
					self.assertIsInstance(a_		,   a_						)
					lowerCAmelCase  :     List[str]       						= np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
					lowerCAmelCase  :     Optional[int]       						= retriever.retrieve(a_		,   n_docs=1						)
					self.assertTrue(out is not None						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= 1
			lowerCAmelCase  :     Dict       						= self.get_dummy_custom_hf_index_retriever(from_disk=a_						)
			lowerCAmelCase  :     str       						= np.array(
			    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     str       						= retriever.retrieve(a_		,   n_docs=a_						)
			self.assertEqual(retrieved_doc_embeds.shape		,   (2, n_docs, self.retrieval_vector_size)						)
			self.assertEqual(len(a_						)		,   2						)
			self.assertEqual(sorted(doc_dicts[0]						)		,   ["embeddings", "id", "text", "title"]						)
			self.assertEqual(len(doc_dicts[0]["id"]						)		,   a_						)
			self.assertEqual(doc_dicts[0]["id"][0]		,   "1"						)  # max inner product is reached with second doc
			self.assertEqual(doc_dicts[1]["id"][0]		,   "0"						)  # max inner product is reached with first doc
			self.assertListEqual(doc_ids.tolist()		,   [[1], [0]]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= self.get_dummy_custom_hf_index_retriever(from_disk=a_						)
			with tempfile.TemporaryDirectory() as tmp_dirname:
					retriever.save_pretrained(a_						)
					lowerCAmelCase  :     List[Any]       						= RagRetriever.from_pretrained(a_						)
					self.assertIsInstance(a_		,   a_						)
					lowerCAmelCase  :     Dict       						= np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
					lowerCAmelCase  :     Optional[Any]       						= retriever.retrieve(a_		,   n_docs=1						)
					self.assertTrue(out is not None						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= 1
			lowerCAmelCase  :     Union[str, Any]       						= self.get_dummy_legacy_index_retriever()
			lowerCAmelCase  :     str       						= np.array(
			    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     str       						= retriever.retrieve(a_		,   n_docs=a_						)
			self.assertEqual(retrieved_doc_embeds.shape		,   (2, n_docs, self.retrieval_vector_size)						)
			self.assertEqual(len(a_						)		,   2						)
			self.assertEqual(sorted(doc_dicts[0]						)		,   ["text", "title"]						)
			self.assertEqual(len(doc_dicts[0]["text"]						)		,   a_						)
			self.assertEqual(doc_dicts[0]["text"][0]		,   "bar"						)  # max inner product is reached with second doc
			self.assertEqual(doc_dicts[1]["text"][0]		,   "foo"						)  # max inner product is reached with first doc
			self.assertListEqual(doc_ids.tolist()		,   [[1], [0]]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= self.get_dummy_legacy_index_retriever()
			with tempfile.TemporaryDirectory() as tmp_dirname:
					retriever.save_pretrained(a_						)
					lowerCAmelCase  :     List[str]       						= RagRetriever.from_pretrained(a_						)
					self.assertIsInstance(a_		,   a_						)
					lowerCAmelCase  :     Optional[Any]       						= np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
					lowerCAmelCase  :     List[str]       						= retriever.retrieve(a_		,   n_docs=1						)
					self.assertTrue(out is not None						)



	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 	_lowerCamelCase     ( self						):
			import torch

			lowerCAmelCase  :     Tuple       						= 1
			lowerCAmelCase  :     Optional[Any]       						= self.get_dummy_canonical_hf_index_retriever()
			lowerCAmelCase  :     Any       						= [[5, 7], [10, 11]]
			lowerCAmelCase  :     List[Any]       						= np.array(
			    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
			lowerCAmelCase  :     List[Any]       						= retriever(a_		,   a_		,   prefix=retriever.config.generator.prefix		,   n_docs=a_						)
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= (
			    out["context_input_ids"],
			    out["context_attention_mask"],
			    out["retrieved_doc_embeds"],
			)
			self.assertEqual(retrieved_doc_embeds.shape		,   (2, n_docs, self.retrieval_vector_size)						)
			self.assertIsInstance(a_		,   a_						)
			self.assertIsInstance(a_		,   a_						)
			self.assertIsInstance(a_		,   np.ndarray						)

			lowerCAmelCase  :     Optional[int]       						= retriever(
			    a_		,   a_		,   prefix=retriever.config.generator.prefix		,   n_docs=a_		,   return_tensors="pt"		,   )
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= (  # noqa: F841
			    out["context_input_ids"],
			    out["context_attention_mask"],
			    out["retrieved_doc_embeds"],
			    out["doc_ids"],
			)
			self.assertEqual(retrieved_doc_embeds.shape		,   (2, n_docs, self.retrieval_vector_size)						)
			self.assertIsInstance(a_		,   torch.Tensor						)
			self.assertIsInstance(a_		,   torch.Tensor						)
			self.assertIsInstance(a_		,   torch.Tensor						)





	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[str]       						= self.get_dpr_ctx_encoder_tokenizer()
			lowerCAmelCase  :     Union[str, Any]       						= 1
			lowerCAmelCase  :     List[Any]       						= self.get_dummy_custom_hf_index_retriever(from_disk=a_						)
			retriever.set_ctx_encoder_tokenizer(a_						)

			lowerCAmelCase  :     str       						= [[5, 7], [10, 11]]
			lowerCAmelCase  :     Any       						= np.array(
			    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]		,   dtype=np.floataa						)
			lowerCAmelCase  :     Optional[Any]       						= retriever(a_		,   a_		,   prefix=retriever.config.generator.prefix		,   n_docs=a_						)

			self.assertEqual(
			    len(a_						)		,   6						)  # check whether the retriever output consist of 6 attributes including tokenized docs
			self.assertEqual(
			    all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")						)		,   a_						)  # check for doc token related keys in dictionary.










'''simple docstring'''






import random
from typing import Any






def   __A    (				a_    :	list						):
		for _ in range(len(a_						)						):
				lowerCAmelCase  :     List[Any]       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase  :     Tuple       						= random.randint(0   ,len(a_						) - 1						)
				lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= data[b], data[a]
		return data


if __name__ == "__main__":
					lowerCAmelCase									=   [0, 1, 2, 3, 4, 5, 6, 7]
					lowerCAmelCase									=   ["""python""", """says""", """hello""", """!"""]
					print("""Fisher-Yates Shuffle:""")
					print("""List""", integers, strings)
					print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



